This document is an example Cygnus Report that can be generated at
the last step of Cygnus data analysis. Each plot shown in this file is
generated when the corresponding R markdown file is knitted to HTML, and
hence can be reused with another Cygnus data to generate a similar
report. All codes are hidden in the final HTML file to simplify the
report.
## CygnusData Object:
## Number of EVs: 107871
## Number of markers: 12
## Present Metadata: stage image
This section includes heatmap of average expressions and
distributions of markers. Based on the distribution, the cutoff for
binary conversion can be determined.
Cygnus offers two methods of marker co-expression analysis. The first requires binary conversion, and simply concerns co-occurrence of different combinations of markers. The benefit of this analysis is that it allows statistical analysis of all possible combination of markers.
The following plot shows pearson correlation coefficients of correlation in expressions of two markers.
## Read the 107871 x 12 data matrix successfully!
## Using no_dims = 3, perplexity = 30.000000, and theta = 0.500000
## Computing input similarities...
## Building tree...
## - point 10000 of 107871
## - point 20000 of 107871
## - point 30000 of 107871
## - point 40000 of 107871
## - point 50000 of 107871
## - point 60000 of 107871
## - point 70000 of 107871
## - point 80000 of 107871
## - point 90000 of 107871
## - point 100000 of 107871
## Done in 18.05 seconds (sparsity = 0.001169)!
## Learning embedding...
## Iteration 50: error is 125.858025 (50 iterations in 32.05 seconds)
## Iteration 100: error is 125.858025 (50 iterations in 45.65 seconds)
## Iteration 150: error is 125.858003 (50 iterations in 43.03 seconds)
## Iteration 200: error is 125.580744 (50 iterations in 39.80 seconds)
## Iteration 250: error is 112.690441 (50 iterations in 32.40 seconds)
## Fitting performed in 192.92 seconds.
Principal Component Analysis finds linear combinations that captures as much variability within the dataset and projects the multi-dimensional data into axes represented by those linaer combinations.
t-distributed stochastic neighbor embedding is a non-linear dimensionality method.